Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the… (More)

We consider the problem of using flow-level data for detection of botnet command and control (C&C) activity. We find that current approaches do not consider timingbased calibration of the C&C traffic… (More)

Recent advances in machine learning have led to innovative applications and services that use computational structures to reason about complex phenomenon. Over the past several years, the security… (More)

In this work, we study the detection of Fast-Flux Service Networks (FFSNs) using DNS (Domain Name System) response packets. We have observed that current approaches do not employ a large combination… (More)

This paper presents a framework for evaluating the transport layer feature space of malware heartbeat traffic. We utilize these features in a prototype detection system to distinguish malware traffic… (More)

The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits,… (More)

Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a… (More)

Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a… (More)